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. 2025 Mar 21;25(1):206.
doi: 10.1186/s12872-025-04584-0.

Predictive value of the triglyceride-glucose index for coronary artery bypass grafting-acute kidney injury patients

Biao Hou  1   2 , Xuejian Hou  1 , Dong Liu  1 , Taoshuai Liu  1 , Kui Zhang  1 , Yang Li  3 , Ran Dong  4
Affiliations

Predictive value of the triglyceride-glucose index for coronary artery bypass grafting-acute kidney injury patients

Biao Hou et al. BMC Cardiovasc Disord. .

Abstract

Background: Acute kidney injury (AKI) is a common and serious complication after coronary artery bypass grafting (CABG), significantly affecting patient outcomes. The triglyceride-glucose (TyG) index, a marker of insulin resistance, has shown potential in predicting various metabolic and cardiovascular conditions. This study aimed to evaluate the predictive value of the TyG index for AKI occurrence following CABG.

Methods: This retrospective, single-center study included 3,260 patients who underwent CABG. Patients were categorized into AKI and non-AKI groups based on postoperative renal function. The preoperative TyG index was calculated from fasting blood glucose and triglyceride levels. Patients were further divided into quartiles based on the TyG index. Logistic regression analysis was used to assess the relationship between TyG index and AKI risk. Subgroup analyses and spline regression were employed to explore potential interactions and non-linear relationships.

Results: Of the 3,260 patients, 514 (15.8%) developed AKI. Baseline characteristics showed that AKI patients had significantly higher levels of hemoglobin (Hb), alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and glucose, while brain natriuretic peptide (BNP) levels were lower compared to non-AKI patients. Logistic regression analysis confirmed that the TyG index was an independent risk factor for AKI following CABG, both as a continuous variable (OR 1.034 [95% CI 1.017-1.050], p < 0.001) and when grouped by quartiles. A non-linear relationship between TyG index and AKI risk was observed, with a significant increase in AKI risk when the TyG index exceeded 5.4. Subgroup analyses revealed that this association was consistent across multiple patient groups, including those stratified by age, sex, BMI, extracorporeal circulation use, and comorbidities such as hypertension, diabetes, and hyperlipidemia.

Conclusions: The preoperative TyG index is a significant independent predictor of AKI after CABG, with a dose-response relationship observed across various subgroups. Monitoring the TyG index can help identify high-risk patients, potentially guiding early intervention and improving postoperative outcomes. These findings underscore the potential of the TyG index as a valuable tool for predicting AKI in clinical practice, warranting further validation in prospective studies.

Keywords: AKI; CABG; Triglyceride-glucose.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Capital Medical University Affiliated Anzhen Hospital (Approval Number: KS2023062). As this is a retrospective study utilizing de-identified clinical data, the requirement for written informed consent was waived by the Ethics Committee. The study protocol adhered to all relevant ethical guidelines and regulations. Participants were provided with detailed information about the study’s purpose, procedures, potential risks, and benefits, and they were given the opportunity to ask questions before providing consent. Ensuring the confidentiality and anonymity of all participants was a top priority throughout the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of patient selection
Fig. 2
Fig. 2
Spline Chart for the TyG index hazard ratio
Fig. 3
Fig. 3
Forest plots for the primary endpoint in different subgroups
See this image and copyright information in PMC

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